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Loops, ladders and links: the recursivity of social and machine learning
Machine learning algorithms reshape how people communicate, exchange, and associate; how institutions sort them and slot them into social positions; and how they experience life, down to the most ordinary and intimate aspects. In this article, we draw on examples from the field of social media to re...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448267/ https://www.ncbi.nlm.nih.gov/pubmed/32863532 http://dx.doi.org/10.1007/s11186-020-09409-x |
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author | Fourcade, Marion Johns, Fleur |
author_facet | Fourcade, Marion Johns, Fleur |
author_sort | Fourcade, Marion |
collection | PubMed |
description | Machine learning algorithms reshape how people communicate, exchange, and associate; how institutions sort them and slot them into social positions; and how they experience life, down to the most ordinary and intimate aspects. In this article, we draw on examples from the field of social media to review the commonalities, interactions, and contradictions between the dispositions of people and those of machines as they learn from and make sense of each other. |
format | Online Article Text |
id | pubmed-7448267 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-74482672020-08-26 Loops, ladders and links: the recursivity of social and machine learning Fourcade, Marion Johns, Fleur Theory Soc Article Machine learning algorithms reshape how people communicate, exchange, and associate; how institutions sort them and slot them into social positions; and how they experience life, down to the most ordinary and intimate aspects. In this article, we draw on examples from the field of social media to review the commonalities, interactions, and contradictions between the dispositions of people and those of machines as they learn from and make sense of each other. Springer Netherlands 2020-08-26 2020 /pmc/articles/PMC7448267/ /pubmed/32863532 http://dx.doi.org/10.1007/s11186-020-09409-x Text en © Springer Nature B.V. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Fourcade, Marion Johns, Fleur Loops, ladders and links: the recursivity of social and machine learning |
title | Loops, ladders and links: the recursivity of social and machine learning |
title_full | Loops, ladders and links: the recursivity of social and machine learning |
title_fullStr | Loops, ladders and links: the recursivity of social and machine learning |
title_full_unstemmed | Loops, ladders and links: the recursivity of social and machine learning |
title_short | Loops, ladders and links: the recursivity of social and machine learning |
title_sort | loops, ladders and links: the recursivity of social and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448267/ https://www.ncbi.nlm.nih.gov/pubmed/32863532 http://dx.doi.org/10.1007/s11186-020-09409-x |
work_keys_str_mv | AT fourcademarion loopsladdersandlinkstherecursivityofsocialandmachinelearning AT johnsfleur loopsladdersandlinkstherecursivityofsocialandmachinelearning |